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Brian Hayes explains how a new technology called compressive sensing slims down data at the source. The Nyquist-Shannon rule has an intuitive rationale with each cycle of an undulating signal having an upward and a downward wiggle. A simple version of compressive sensing works something like the coin-puzzle algorithm, 'weighing' various subsets of the N-element signal vector. A prehistory of compressive sensing has also come to light, particularly in the earth sciences. Kevin F. Kelly and his colleagues at Rice University have built a series of compressive-sensing cameras. At the heart of these devices is an array of micro-mirrors that can be independently switched between two positions. Another area where compressive sensing has promise is magnetic resonance imaging. The imperative in MRI is not much compressing data for storage but acquiring it quickly, because the patient must hold still while an image is formed.